Abstract
One of the most severe threats to revenue and quality of service in telecom providers is fraud. The advent of new technologies has provided fraudsters new techniques to commit fraud. SIM box fraud is one of such fraud that has emerged with the use of VOIP technologies. In this work, a total of nine features found to be useful in identifying SIM box fraud subscriber are derived from the attributes of the Customer Database Record (CDR). Artificial Neural Networks (ANN) has shown promising solutions in classification problems due to their generalization capabilities. Therefore, supervised learning method was applied using Multi layer perceptron (MLP) as a classifier. Dataset obtained from real mobile communication company was used for the experiments. ANN had shown classification accuracy of 98.71 %.
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Acknowledgments
The authors first thank the anonymous reviewers for their valuable comments and to Universiti Teknologi Malaysia (UTM) for the FRGS Grant Vote number 4F086 that is sponsored by Ministry of Higher Education (MOHE) and Research Management Centre, Universiti Teknologi Malaysia, Skudai, Johor.
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Elmi, A.H., Ibrahim, S., Sallehuddin, R. (2013). Detecting SIM Box Fraud Using Neural Network. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_69
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DOI: https://doi.org/10.1007/978-94-007-5860-5_69
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